plot_ly() generating interactive graphsshiny for interactive visualizationsflexdashboard for creating interactive dashboardslibrary(tidyverse)
library(knitr)
library(broom)
library(stringr)
library(modelr)
library(forcats)
library(haven)
library(plotly)
options(digits = 3)
set.seed(1234)
theme_set(theme_minimal())We previously discussed using plotly and the ggplotly() function to modify existing ggplot() objects. However we can also build plotly.js objects directly using the plot_ly() function. It is based on the layered grammar of graphics, but instead it directly calls components of the plotly.js JavaScript library to generate graphs. Originally it was intended to be used for plot types not supported by ggplot2 (like 3d graphs), but has been extended so that you could use it entirely instead of ggplot2.
txhousing## # A tibble: 8,602 × 9
## city year month sales volume median listings inventory date
## <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Abilene 2000 1 72 5380000 71400 701 6.3 2000
## 2 Abilene 2000 2 98 6505000 58700 746 6.6 2000
## 3 Abilene 2000 3 130 9285000 58100 784 6.8 2000
## 4 Abilene 2000 4 98 9730000 68600 785 6.9 2000
## 5 Abilene 2000 5 141 10590000 67300 794 6.8 2000
## 6 Abilene 2000 6 156 13910000 66900 780 6.6 2000
## 7 Abilene 2000 7 152 12635000 73500 742 6.2 2000
## 8 Abilene 2000 8 131 10710000 75000 765 6.4 2001
## 9 Abilene 2000 9 104 7615000 64500 771 6.5 2001
## 10 Abilene 2000 10 101 7040000 59300 764 6.6 2001
## # ... with 8,592 more rows# ggplot
p <- ggplot(txhousing, aes(date, median)) +
geom_line(aes(group = city), alpha = 0.2)
ggplotly(p)# initialize plotly object
p <- txhousing %>%
group_by(city) %>%
plot_ly(x = ~date, y = ~median)
p# specify geometric relation (i.e. what kind of geom to draw?)
# add a line highlighting houston
add_lines(
# plots one line per city since p knows city is a grouping variable
add_lines(p, alpha = 0.2, name = "Texan Cities", hoverinfo = "none"),
name = "Houston", data = filter(txhousing, city == "Houston")
)# redone completely with piping
allCities <- txhousing %>%
group_by(city) %>%
plot_ly(x = ~date, y = ~median) %>%
add_lines(alpha = 0.2, name = "Texan Cities", hoverinfo = "none")
allCities %>%
filter(city == "Houston") %>%
add_lines(name = "Houston")We can add “layers” by using the piped notation used by dplyr and other tidyverse packages (not + in ggplot2).
See here.
flexdashboardflexdashboard is a package for R that allows you to create visual dashboards. You can incorporate tables, graphs (both static and dynamic), text, and all sorts of components to provide insight and analysis. Layouts are customizable by dividing the page into columns and rows.
The entire dashboard is basically one large R Markdown document, so you can use the same syntax and code/output/analysis format that you’ve used before (though generally hiding the code with echo = FALSE).
flexdashboardOne concern with a traditional dashboard is that while it may be “interactive”, it is not dynamic - that is, you cannot update the dashboard by changing parameters or underlying data values. Shiny applications allow you to do so, but they are very clunky to write due to the deeply nested structure of the code. Instead, we can combine the two approaches by generating a Shiny dashboard. In our header, we simply add runtime: shiny to convert the standard dashboard to a Shiny dashboard.
devtools::session_info()## setting value
## version R version 3.3.3 (2017-03-06)
## system x86_64, darwin13.4.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz America/Chicago
## date 2017-05-03
##
## package * version date source
## assertthat 0.2.0 2017-04-11 cran (@0.2.0)
## backports 1.0.5 2017-01-18 CRAN (R 3.3.2)
## broom * 0.4.2 2017-02-13 CRAN (R 3.3.2)
## colorspace 1.3-2 2016-12-14 CRAN (R 3.3.2)
## DBI 0.6 2017-03-09 CRAN (R 3.3.3)
## devtools 1.12.0 2016-06-24 CRAN (R 3.3.0)
## digest 0.6.12 2017-01-27 CRAN (R 3.3.2)
## dplyr * 0.5.0 2016-06-24 CRAN (R 3.3.0)
## evaluate 0.10 2016-10-11 CRAN (R 3.3.0)
## forcats * 0.2.0 2017-01-23 CRAN (R 3.3.2)
## foreign 0.8-67 2016-09-13 CRAN (R 3.3.3)
## ggplot2 * 2.2.1.9000 2017-05-01 Github (hadley/ggplot2@f4398b6)
## gtable 0.2.0 2016-02-26 CRAN (R 3.3.0)
## haven * 1.0.0 2016-09-23 cran (@1.0.0)
## hms 0.3 2016-11-22 CRAN (R 3.3.2)
## htmltools 0.3.6 2017-04-28 cran (@0.3.6)
## htmlwidgets 0.8 2016-11-09 CRAN (R 3.3.1)
## httr 1.2.1 2016-07-03 CRAN (R 3.3.0)
## jsonlite 1.4 2017-04-08 cran (@1.4)
## knitr * 1.15.1 2016-11-22 cran (@1.15.1)
## lattice 0.20-34 2016-09-06 CRAN (R 3.3.3)
## lazyeval 0.2.0 2016-06-12 CRAN (R 3.3.0)
## lubridate 1.6.0 2016-09-13 CRAN (R 3.3.0)
## magrittr 1.5 2014-11-22 CRAN (R 3.3.0)
## memoise 1.0.0 2016-01-29 CRAN (R 3.3.0)
## mnormt 1.5-5 2016-10-15 CRAN (R 3.3.0)
## modelr * 0.1.0 2016-08-31 CRAN (R 3.3.0)
## munsell 0.4.3 2016-02-13 CRAN (R 3.3.0)
## nlme 3.1-131 2017-02-06 CRAN (R 3.3.3)
## plotly * 4.6.0 2017-04-25 CRAN (R 3.3.3)
## plyr 1.8.4 2016-06-08 CRAN (R 3.3.0)
## psych 1.7.3.21 2017-03-22 CRAN (R 3.3.2)
## purrr * 0.2.2 2016-06-18 CRAN (R 3.3.0)
## R6 2.2.0 2016-10-05 CRAN (R 3.3.0)
## Rcpp 0.12.10 2017-03-19 cran (@0.12.10)
## readr * 1.1.0 2017-03-22 cran (@1.1.0)
## readxl 0.1.1 2016-03-28 CRAN (R 3.3.0)
## reshape2 1.4.2 2016-10-22 CRAN (R 3.3.0)
## rlang 0.0.0.9018 2017-05-01 Github (hadley/rlang@460323e)
## rmarkdown 1.3 2016-12-21 CRAN (R 3.3.2)
## rprojroot 1.2 2017-01-16 CRAN (R 3.3.2)
## rvest 0.3.2 2016-06-17 CRAN (R 3.3.0)
## scales 0.4.1 2016-11-09 CRAN (R 3.3.1)
## stringi 1.1.2 2016-10-01 CRAN (R 3.3.0)
## stringr * 1.2.0 2017-02-18 CRAN (R 3.3.2)
## tibble * 1.3.0.9001 2017-05-01 Github (tidyverse/tibble@08af6b0)
## tidyr * 0.6.1 2017-01-10 CRAN (R 3.3.2)
## tidyverse * 1.1.1 2017-01-27 CRAN (R 3.3.2)
## viridisLite 0.2.0 2017-03-24 cran (@0.2.0)
## withr 1.0.2 2016-06-20 CRAN (R 3.3.0)
## xml2 1.1.1 2017-01-24 CRAN (R 3.3.2)
## yaml 2.1.14 2016-11-12 cran (@2.1.14)